Hu, Zhan; J. Zhou; C. Wang; H. Wang; Z. He et. al. (2020): Data presented in the paper: A Novel Instrument for Bed Dynamics Observation Supports: Machine Learning Applications in Mangrove Biogeomorphic Processes. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:3d971ec0-7a0d-46fa-be02-a2b3d4b9badd

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Categories

• Land and Water Management
• Oceanography
• Physical Geography and Environmental Geoscience

Keywords

Bed-level dynamics Laster based Surface Elevation Dynamics (LSED)-sensor Machine learning Tidal flats

Geolocation

view on openstreetmap

Time coverage

2018/2019

Licence

CC0

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by Zhan Hu, J. Zhou, C. Wang , H. Wang, Z. He, Y. Peng, P. Zheng, F. Cozzoli, T.J. (Tjeerd) Bouma

Short-term bed level dynamics on the intertidal flats plays a critical role in long-term coastal wetland dynamics. High-frequency observation techniques are crucial for better understanding of intertidal biogeomorphic evolutions. Here, we introduce an innovative instrument for bed dynamics observation, i.e. LSED-sensor (Laster based Surface Elevation Dynamics sensor). LSED-sensors inherit the merits of the previously-introduced optical SED-sensors as it enables continuous long-term monitoring with relatively low cost of labor and acquisition. By adapting Laster-ranging technique, LSED-sensors avoid touching the measuring object (i.e. bed surface) and they do not rely on daylights, as it is for the optical SED-sensors. Furthermore, the new LSED-sensors are equipped with a real-time data transmission function, enabling creating automatic observation networks covering multiple (remote) sites. During a 21-days field survey in a mangrove wetland, good agreement (R2=0.7) has been obtained between the automatic LSED-sensor measurement and an accurate ground-truth measurement method, i.e. Sedimentation Erosion Bars. The obtained LSED-sensor data was subsequently used to develop machine learning predictors, which revealed the main drivers of the accumulative and daily bed level changes. We expect that the LSED-sensors can further support machine learning applications to extract new knowledge on coastal biogeomorphic processes.

History

• 2020-05-24 first online, published, posted

Publisher

4TU.Centre for Research Data

Format

media types: application/vnd.openxmlformats-officedocument.wordprocessingml.document, application/x-matlab-data, application/zip, text/plain

Funding

• EPSRC, EP/R024537/1
• NSFC, 51761135022
• NWO, ALWSD.2016.026
• Sustainable Deltas

Organizations

Department of Geography, Trinity College Dublin;
Department of Geography, University of Cambridge;
Department of Physical Geography, Utrecht University;
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente;
Guangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering, and School of Marine Science, Sun Yat-sen University;
HKV Consultants;
Institute of Environmental and Ecological engineering, Guangdong University of Technology;
NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine and Delta Systems and Utrecht University;
Research Institute for Nature and Forest (INBO);
Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, and State Environmental Protection Key Laboratory of Satellite Remote Sensing;
School of Marine Engineering and Technology, Sun Yat-Sen University, Guangzhou;
Southern Marine Science and Engineering Guangdong Laboratory;
TU Delft, Faculty of Civil Engineering and Geosciences;
University of Antwerp, Ecosystem Management Research Group;
Water Engineering and Management, University of Twente